Handwriting recognition systems are trained components, meaning that their classification techniques use machine learning algorithms that require a representative “teaching set” of ink samples in order to perform recognitions. A large amount of teaching data must typically be collected in order to generate an accurate recognizer in a particular language. Such data collection can take a large amount of time in order to gather a sufficient quantity of the right type. Furthermore, data collection in one language does not scale to additional languages. In other words, the data collection process must typically be repeated for each language for which to recognize. For one or more of these reasons, the process of creating an accurate handwriting recognizer for one or more languages can be very time consuming and difficult to scale.